The rapid financing of the AI boom through massive corporate debt issuance is creating significant stress on global financial markets. This influx of private capital forces competition with government Treasury bonds, as AI companies offer higher yields than equivalent sovereign debt, thereby pushing up long-term interest rates. While this signals strong investment demand for AI, it raises concerns about systemic risk and the potential destabilization of core bond markets. Policymakers must navigate the tension between fueling critical technological growth and maintaining stable public borrowing costs to prevent a financial crisis.
Updating California’s Adverse Childhood Experiences (ACEs) and Toxic Stress Risk Assessment and Response Algorithm
English Summary
This RAND report details the update of California’s ACEs and Toxic Stress Risk Assessment Algorithm, driven by a need to incorporate the latest scientific understanding and clinical experience. The update, informed by a committee of ten experts and usability testing with 97 California clinicians, refines the algorithm to prioritize trauma-informed care, emphasize protective factors, and account for a broader range of adverse experiences beyond the original ten ACE categories. Clinician accuracy in assessing risk and selecting appropriate clinical responses averaged 88-89% during testing, highlighting the algorithm’s potential for practical application. The updated algorithm aims to improve early identification and response to toxic stress physiology, ultimately contributing to better health outcomes for children and adults.
中文摘要
本 RAND 報告詳述了加州 ACE（應對性創傷）和壓力風險評估演算法的更新，該更新旨在納入最新的科學理解和臨床經驗。由十名專家委員會和 97 名加州臨床醫生進行的可用性測試所支持的更新，改進了演算法，以優先考慮以創傷為導向的照護、強調保護因素，並考慮超出最初的十個 ACE 類別的更廣泛的負面經歷。在測試過程中，臨床醫生評估風險和選擇合適的臨床反應的準確度平均為 88-89%，突顯了演算法在實踐應用方面的潛力。更新後的演算法旨在改進對有毒壓力生理學的早期識別和反應，從而最終改善兒童和成人的健康結果。
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